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Paper Citation Record · LEDGER

Do Theory of Mind Benchmarks Need Explicit Human-like Reasoning in Language Models?

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2504.01698.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2504.01698 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:26:40.632447Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T12:29:51.425966Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 007e2f8e-39e2-475c-8829-2f9866b79efa · inbound

Small LLMs Do Not Learn a Generalizable Theory of Mind via Reinforcement Learning cites this paper.

Small LLMs Do Not Learn a Generalizable Theory of Mind via Reinforcement Learning Do Theory of Mind Benchmarks Need Explicit Human-like Reasoning in Language Models?

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T15:26:40.632447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T15:26:40.632447Z digest=sha256:1c80e7707f06eab90161ab7d9feda952e810e94ccb892b25a497098604a0dc9c

Observation b2660240-6b57-40f8-b57e-407c33583bf4 · inbound

Does Theory of Mind Improvement Really Benefit Human-AI Interactions? Empirical Findings from Interactive Evaluations cites this paper.

Does Theory of Mind Improvement Really Benefit Human-AI Interactions? Empirical Findings from Interactive Evaluations Do Theory of Mind Benchmarks Need Explicit Human-like Reasoning in Language Models?

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-05-19T17:57:42.634295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-19T17:55:04.458343Z digest=sha256:0181cee407a0abfe74a99f9b98c04c61e41db303f08002863f1176be092cc94c

Observation 5707d8eb-d5e7-4ae7-87df-9bbbf4204a37 · inbound

MindZero: Learning Online Mental Reasoning With Zero Annotations cites this paper.

MindZero: Learning Online Mental Reasoning With Zero Annotations Do Theory of Mind Benchmarks Need Explicit Human-like Reasoning in Language Models?

Reference 67

Resolution
verified exact
arxiv_id, observed 2026-07-01T19:56:10.613982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-28T21:57:17.194711Z digest=sha256:db35df249cadd7e3a0407844ef18700005f1627821a10c9d7ff28bf63e31fff9

Observation 1c659c40-92f6-423f-9f5d-faa994f78379 · inbound

From Shortcuts to Reasoning: Robust Post-Training of Theory of Mind with Reinforcement Learning cites this paper.

From Shortcuts to Reasoning: Robust Post-Training of Theory of Mind with Reinforcement Learning Do Theory of Mind Benchmarks Need Explicit Human-like Reasoning in Language Models?

Reference 96

Resolution
verified exact
arxiv_id, observed 2026-07-02T23:57:28.191115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-27T17:42:38.122144Z digest=sha256:4edfa9d5ee1392997b433329b12290c47d45096b1f87232e5a49142448f7e886

Observation 3b7e11e6-a7af-495a-b9d8-8357b08744c5 · inbound

Embodied Explainability and Ontological Obstacles: Why We Struggle to Explain the Answers of Large Language Models (LLMs) cites this paper.

Embodied Explainability and Ontological Obstacles: Why We Struggle to Explain the Answers of Large Language Models (LLMs) Do Theory of Mind Benchmarks Need Explicit Human-like Reasoning in Language Models?

Reference 78

Resolution
verified exact
arxiv_id, observed 2026-07-04T12:29:51.427875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-06-26T06:54:48.941504Z digest=sha256:e5eaf6247c1b16c9617228f863d2dfe5f9173af99bd0a3e95d4cbb401aba39b1